What is data?
Data is everywhere, like sports scores (think about your favourite sport and the last competition you have watched), school grades and even your favourite video game points. And did you know that we also generate a lot of data? When we use our computers, tablets, and phones, we often generate data that can be collected. For example, every time we use our phones to call someone or take a photo. Social media is another good example. When we post photos, like our friends’ posts or share content, platforms like Instagram and TikTok creates data about your interests and activities. But what exactly is data and how can we make sense of it? That’s the big question!
Data is information that has been collected from many sources. Numbers, words, measurements, and even descriptions of objects can all be included. For instance, your school grades are data. Data makes patterns and trends easier to see, which aids in our understanding of the environment. But without the correct resources, interpreting large amounts of data may be intimidating and challenging. For example, have a look at Figure 1, a table listing the grades attained in English and Mathematics by different students during winter term. Can you identify who had the highest grade in Mathematics? How about in English?
The data displayed on the table above four different variables: Student Name, Term English Grade and Maths Grade. Let’s take the data point for Benjamin, as an example: it has four different values. We can see that his name is Benjamin, the term Winter, his grade in English was 80 and his grade in Maths was 95. All the information is displayed on a table format, so all the information about Benjamin is distributed along 4 columns and 1 row. The same happens for the other students.
There are different types of data. One is numerical data (also known as quantitative data), and it represents the count or measure of something using numbers. The school grades, your siblings’ height and your friends’ shoes size are examples of numerical data. Percentages and prices also represent numerical data.
The other type is categorical data (also known as nominal data), data that represents characteristics or attributes that can be divided into different categories. This type of data has non-numerical nature, though numbers can be used as labels.
On the table illustrated on Figure 1, for example, the variable Student Name and Term are two examples of categorical data because they do not measure quantity of anything. Seasons or terms of year, post codes, colours, clothes brands, and types of animals are also classified as categorical data. Categorical data don’t have a particular order and one category is not more or less than another.
Interpreting Data
Interpreting data means figuring out what information is trying to tell us. Imagine you have a diary where you note down the temperature each day. At the end of the month, you look at all the temperatures you’ve written and notice a pattern. If it’s been getting warmer, you might say, “It looks like the weather is getting hotter as we move closer to summer.” That’s you interpreting data. Another example is when you play video games.
Let’s say you want to know which game you are best at. You could keep track of your scores for each game over a week. At the end, you compare the scores. If you consistently score higher in one game, you can conclude that it’s the game you’re best at. This conclusion is based on interpreting the scores you’ve collected. Let’s take a look at other examples.
Think about your school and grades. If you notice that you get better marks in subjects where you do your homework regularly, you might decide that doing homework helps you understand the subject better. Here, you’ve used your grades (data) to figure out that doing homework (a pattern) improves your performance.
Interpreting data is like being a detective. You collect clues and look for patterns to understand what’s going on. This skill helps you make decisions and solve problems, whether it’s about your hobbies, schoolwork, or understanding your friends better. By analysing and interpreting data, you can make smarter and better-informed choices.
The next chapter is Bias and representation: why should we worry about them when working with data?
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